The Taylor Rule and Forecast Intervals for Exchange Rates

In this paper, we examine the Meese–Rogoff puzzle from a different perspective: out‐of‐sample interval forecasting. While most studies in the literature focus on point forecasts, we apply semiparametric interval forecasting to a group of exchange rate models. Forecast intervals for 10 OECD exchange rates are generated and the performance of the empirical exchange rate models are compared with the random walk. Our contribution is twofold. First, we find that in general, exchange rate models generate tighter forecast intervals than the random walk, given that their intervals cover out‐of‐sample exchange rate realizations equally well. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting distributions of exchange rates. We also find that the benchmark Taylor rule model performs better than the monetary, PPP and forward premium models, and its advantages are more pronounced at longer horizons. Second, the bootstrap inference framework proposed in this paper for forecast interval evaluation can be applied in a broader context, such as inflation forecasting.     

Forecasting the exchange rate: A monetary or random walk phenomenon?

This paper evaluates the forecasting accuracy of monetary and random walk models of the exchange rate. Instrumental-variable estimates of the ‘simple’ monetary model are not supported by the data, while the full-information-maximum-liklihood estimates of its rational-expectations counterpart are. The rational-expectations monetary model is operationalized using the results of Hansen and Sargent (1980) and Flavin (1981) and Box-Jenkins time series techniques. Monthly data on the US and UK economics over the recent flexible exchange rate period serve as the case study.     

Floating exchange rates,expectations and new information

This paper analyzes the relationship between forward exchange rates, future spot rates and new information. A stochastic model of exchange rate determination is used to formally show how unanticipated changes in the exchange rate determinants (or ‘news’) affect the spot rate. The empirical analysis indicates that ‘new information’ plays an important role in explaining the market forecasting error, or difference between the spot rate and the forward rate, determined in the previous period.     

Evaluating forecasts from SETAR models of exchange rates

We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.     

Nonlinear dynamics in foreign exchange rates

This paper investigates whether the behavior of real and nominal foreign exchange rates as well as interest rates are governed by nonlinear dynamics; it also explores whether observed deviations from parity conditions exhibit nonlinear dependence. Standard statistical tests for randomness, such as autocorrelation tests, have low power against a large class of deterministic, nonlinear processes. Discerning nonrandomness of innovations in exchange rates is important for a variety of reasons. For example, many models of international asset pricing assume exchange rates to follow a random walk. Furthermore, nonlinear patterns in deviations from various exchange rate parities have implications for the existence of a time-varying foreign exchange risk premium. With the use of the BDS statistic and a correlation dimension analysis, this paper's primary findings are that (1) foreign exchange markets have become increasingly complex and therefore less amenable to forecasting over time; (2) although forward exchange risk premia are statistically significant and display a deterministic structure, this structure is complex and therefore not easily discernible; and (3) innovations in real exchange rates are consistent with a Purchasing Power Parity equilibrium.     

Does the exchange rate follow a random walk? A Monte Carlo study of four tests for a random walk

Several authors have urged that real and nominal exchange rates follow a random walk. Conditions under which the exchange rate follows a random walk are reviewed in this paper. Many of these do not appear to hold. Therefore, the empirical evidence presents a puzzle : some evidence suggets the exchange rate follows a random walk, while other evidence suggests the opposite should be true. This paper attempts to reconcile the evidence. It is argued that the tests for a random walk have low power. Monte Carlo evidence on four tests for a random walk, is presented that suppors this claim.     

Structural models of exchange rate determination

This study compares the forecasting accuracy of state space techniques based on the monetary models of exchange rate with univariate and random walk models for four countries. It is found that these structural models outperform ARIMA and random walk models for all four countries. A state space vector that contains variables based on the monetary model easily outperforms random walk as well as ARIMA models for France, Germany, UK, and Japan during the sample period of this study.     

Efficiency and a simple model of exchange-rate determination

A monetary model has no intrinsic dynamics so the intertemporal behavior of exchange rates, spot and forward, depend entirely upon the time processes of the exogenous variables. For typical values of interest elasticities of money demand, only if all shocks are permanent do exchange rates follow random walks.Recently, changes in relative outputs and money supplies in the Canadian–US context have evidenced serial correlation so that shocks are not exclusively permanent ones. None the less, exchange rates have followed a random walk. This suggests that the monetary model is an inappropriate specification and that models which pay attention to the choice of currency of denomination deserve greater attention.     

民币对美元日汇率决定模型研究

基于汇率决定理论的最新研究进展．本文分析了人民币对美元日汇率的影响因素。研究发现,宏观经济新闻、外汇市场微观因素买卖价差是人民币对美元日汇率的重要影响因素,而中美相对利率并不是人民币对美元日汇率的影响因素。进一步,本文将随机游走模型、新闻变量以及外汇市场微观因素结合起来,构建了人民币对美元日汇率决定理论模型。实证研究发现,人民币对美元日汇率由自身滞后值、中国经济活动方面的新闻和零售市场买卖价差决定。     

The influence of the forecast horizon on judgemental probability forecasts of exchange rate movements

An experiment is reported which compares directional forecasting performance of experts, novices and simple statistical models over three time horizons on a task involving probabilistic forecasts of exchange rate movements. Probability-judgement accuracy analyses illustrated no clear overall performance differences between experts and novices, but significant differences between the groups on various important components of judgement suggested that the groups obtained their similar overall scores using different cognitive strategies. Striking horizon effects and expertize–horizon interactions were also observed. The subjects performed better than a random walk forecaster, but worse than the random walk with constant drift and first-order autoregressive models. Composite human judgement, however, not only improved on individual judgement but, also, surpassed the simple statistical models in many instances. Possible explanations are offered for these results, suggestions are made for future research, and practical implications are emphasized.     

THE RESPONSE OF FORWARD EXCHANGE RATES TO INTEREST RATE FORECASTING ERRORS

This paper examines the effects of interest rate news on changes in forward foreign exchange rates. Virtually none of the errors in forecasting forward exchange rates are explained by interest rate forecasting errors. The results are consistent with a conjecture that the forward exchange rate is not an estimate of the expected spot exchange rate.     

Exchange rate expectations and the pricing of Chinese cross-listed stocks

I show that the price discounts of Chinese cross-listed stocks (American Depositary Receipts (ADRs) and H-shares) to their underlying A-shares indicate the expected yuan/US dollar exchange rate. The forecasting models reveal that ADR and H-share discounts predict exchange rate changes more accurately than the random walk and forward exchange rates, particularly at long forecast horizons. Using panel estimations, I find that ADR and H-share investors form their exchange rate expectations according to standard exchange rate theories such as the Harrod-Balassa-Samuelson effect, the risk of competitive devaluations, relative purchasing power parity, uncovered interest rate parity, and the risk of currency crisis.     

Efficient exchange rate forecasts: Lagged models better than the random walk

The evidence of Meese amd Rogoff (1983) on the out-of-sample forecasting performance of structural exchange rate models in comparison to the random walk model portrays a disappointing picture of structural models. This paper re-considers the issue for the German mark for an updated period to include a larger set of structural models and lagged adjustment. Besides out-of-sample evidence, in-sample evidence is also examined. We conclude that while some stuctural models dominate the random walk, a lagged adjustment consideration can contribute towards better performance.     

Forward and spot exchange rates in a bivariate TAR framework

Structural exchange rate models explain only a small part of the movements in dollar exchange rate. Recent empirical work has focused on the failure to account for nonlinearities in the data generating mechanism, as an explanation of this bad performance. Here two bivariate threshold autoregressive models for the spot and forward exchange rates are considered. In the first model the regimes are determined by the log difference of the two rates; in the second one the regimes are driven by the forward spot no-arbitrage condition. These processes are able to capture the ‘swing’ behaviour observed in the exchange rate market. Finally the forecasting ability of the models for the dollar/DM exchange rate is evaluated by stochastic simulation.     

Investment intensity of currencies and the random walk hypothesis: Cross-currency evidence

This paper studies the cross-currency and temporal variations in the random walk behavior in exchange rates. We characterize currencies with relatively large investment flows as investment intensive and conjecture that the more investment intensive a currency is, the closer its exchange rate adheres to random walk. Using 29 floating bilateral USD exchange rates, we find that the higher the investment intensity, the less likely it is to reject random walk and the smaller the deviation from random walk is. However, the effect of investment intensity is non-monotonic. Application of threshold models shows that after investment intensity reaches the estimated thresholds, the level of investment intensity has no further effect on the deviation from random walk. These findings help reconcile the previous conflicting results on the random walk in exchange rates by focusing on the effect of cross-currency and temporal variations in investment intensity.     

The norman conquest of $4.86 and the asset approach to the exchange rate

In this paper the asset approach to the exchange rate is tested for the pound–dollar exchange rate, using quarterly data, over the period 1921 to 1925. The out-of-sample forecasting performance of the asset approach is compared to a purchasing power parity model and random walk representation of the exchange rate. The results are shown to be broadly supportive of similar tests conducted for the 1970s experience with floating exchange rates.     

New evidence on nominal exchange rate predictability

The Meese–Rogoff puzzle, one of the well-known puzzles in international economics, concerns the weak relationship between nominal exchange rates and market fundamentals. The purpose of this paper is to show that market fundamentals do in fact matter in forecasting nominal exchange rates. In particular, we emphasize the importance of the Harrod–Balassa–Samuelson effect in modeling deviations from purchasing power parity. Based on the post-Bretton Woods period, we provide solid out-of-sample evidence that rejects the random walk forecast model at medium-term and long-term forecast horizons. We also find mild evidence for out-of-sample predictability of nominal exchange rates over the short term.     

A new perspective on post‐earnings‐announcement‐drift: Using a relative drift measure

Prior research finds that there is a delayed reaction to both analyst‐based earnings surprises and random‐walk‐based earnings surprises. Focusing on the market reaction from the post‐announcement window, prior studies show that analyst‐based drift is larger than random walk‐based drift. This finding is counter‐intuitive if we believe large, sophisticated investors tend to trade on analysts’ forecast earnings news and thus react faster and more completely than smaller and less sophisticated investors react to random walk earnings news. In this study, we construct a relative measure of post‐earnings‐announcement drift (PEAD) (i.e., drift as a proportion of total market reaction to earnings news) which we refer to as the ‘drift ratio’, and we provide evidence, consistent with our intuition, that analyst‐based drift ratio is smaller (not greater) than random‐walk‐based drift ratio. We find that this difference is more pronounced in more recent periods and for firms with more sophisticated investors. Our approach to measure the PEAD is more intuitive than that in traditional PEAD literature. Our results thus complement existing research findings by utilizing the drift ratio measure to generate new insights about the drift phenomenon.     

Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices,commodities and exchange rates: Inter-day versus intra-day data

In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively. This paper is based on the recommendations of the Basel Committee on Banking Supervision. Regarding the forecasting performances, the exploitation of intra-day information does not appear to improve the accuracy of the VaR and ES forecasts for the 10-steps-ahead and 20-steps-ahead for the 95%, 97.5% and 99% significance levels. On the contrary, the GARCH specification, based on the inter-day information set, is the superior model for forecasting the multiple-days-ahead VaR and ES measurements. The intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock indices, commodities and exchange rates.The multi-period VaR and ES forecasts are estimated for a range of datasets (stock indices, commodities, foreign exchange rates) in order to provide risk managers and financial institutions with information relating the performance of the inter-day and intra-day volatility models across various markets. The inter-day specification predicts VaR and ES measures adequately at a 95% confidence level. Regarding the 97.5% confidence level that has been recently proposed in the revised 2013 version of Basel III, the GARCH-skT specification provides accurate forecasts of the risk measures for stock indices and exchange rates, but not for commodities (that is Silver and Gold). In the case of the 99% confidence level, we do not achieve sufficiently accurate VaR and ES forecasts for all the assets.     

Semi-rational expectations and exchange-rate dynamics

The Rational Expectations Hypothesis (REH) asserts that, on average, the economic agents are accurate in predicting future economic developments. The paper demonstrates, however, that in a world of costly information, individual rationality may result in consistent and persistent forecasting biases. A distinction is drawn between perfect foresight or efficient forecasting—which is consistent with the REH—and myopic perfect foresight—which is the profit maximizing, and thus the rational one from an individualistic point of view, even though the latter may result in persistently biased forecasting. These concepts are illustrated in a model of exchange rate dynamics which introduces myopic or ‘semi’ rationality into Dornbusch's familiar model.